Current state-of-the-art technology cannot deliver quality video in real-time at a reasonable cost over the Internet. There is a fundamental reason for this situation; the methods use algorithms that cannot compress the video and the audio signals to the levels required for economical transmission bandwidth consumption without destroying the quality of the decompressed signals at the receiving end. Quality that is not comparable to cable TV is not acceptable. There is only limited market demand for it.
Current methods do not provide sufficient speed necessary to provide desirable and economical levels of compression. The video currently available on the Internet consists of short sequences that must be downloaded first before being played back. The amount of data involved in video signals is so large that software implementations of current algorithms cannot process them in real-time.
Prior art attempts to provide rapid, high-quality video/audio compression have met with limited success.
U.S. Pat. No.5,761,341 discloses a method of image compression based on the Wavelet Transformation of the given image using both low frequency and high frequency coefficients in the decompression process. No mention is made of any method to recover the image directly from the low frequency WT coefficients alone which is one of the innovations of this invention.
The paper, “Image Data Compression with Selective Preservation of Wavelet Coefficients,” Atsumi Eiji et. al, Visual Communications and Image Processing '95, Taipei, Taiwan, Proceedings of the SPIE, Vol. 2501. 1995 describes a method for image compression that is also based on the Wavelet Transform. The main thrust of the paper is in two techniques for deciding which high frequency coefficients to keep to achieve optimum quality for a given level of compression for the decompressed image. No mention is made about what to do when no high frequency coefficients are available.
The paper, “Haar Wavelet Transform with Interband Prediction and its Application to Image Coding,” Kukomi N. et al, Electronics and Communications in Japan, Part III—Fundamental Electronic Science, Vol. 78, No. 4, April 1995, herein incorporated fully by reference, describes another method for image compression that uses the Haar wavelet as the basis for the Wavelet Transform. The Haar wavelet is used because of the simple functional forms used to obtain the low and high frequency WT coefficients, i.e., the sum and the difference divided by 2 of two consecutive pixels. Because of these simple relationships, it is postulated that the high frequency coefficients and the first order derivative of the low frequency coefficients are linearly related with a proportionality variable α. Using this linear function to predict the high frequency coefficients from the low frequency coefficients, the error between the actual and predicted high frequency coefficient values can be obtained and the value of a used is the one that minimizes the mean squared error. Thus, instead of encoding the low and the high frequency coefficients, the method consists of encoding the low frequency coefficients and the error between the predicted and the actual high frequency coefficients which presumably reduces the bit rate somehow. This method cannot work for any other type of wavelet and is therefore of limited value.
The paper, “Sub-band Prediction using Leakage Information in Image Coding,” Vaisey, IEEE Transactions on Communications, Vol 43, No. 2/04, Part 01, February 1995, incorporated herein fully by reference, describes a method for image sub-band coding that attempts to predict the high-pass bands from the low-pass bands and then encodes the error between the predicted and actual high-pass bands which requires fewer bits than encoding the actual high-pass bands. The prediction is done by examining a 3×3 neighborhood around each pixel in a given low frequency band and classifying it into one of 17 groups. The result of the classification is then used to choose a family of 9 high frequency coefficient predictors that depend on the appropriate high-pass band. This method suffers from the basic shortcoming of all vector quantization methods: it is not general enough and thus, cannot provide the flexibility necessary to provide rapid, high-quality compression and decompression that can adapt to the wide variety of images characteristic of current video productions.
The paper, “Image Restoration using Biorthogonal Wavelet Transform,” Bruneau, J. M. et al, Visual Communications and Image Processing '90, Lausanne, Switzerland, Proceedings of the SPIE, Vol. 1360, 1990, herein incorporated fully by reference, discloses a method of image restoration based on the non-decimated biorthogonal Wavelet Transform. The only thing in common between this paper and the description of the invention is the basic wavelet theory math used and a few similarities in some of the notation, which is not surprising since the notation used on most papers discussing wavelets is the one introduced by their inventor, I. Daubechies (see, for example, “Ten Lectures on Wavelets,” I. Daubechies, Society for Industrial and Applied Mathematics, Philadelphia, 1992.), herein incorporated fully by reference. The method presented in the paper can only be used for deblurring images that have been exposed to a blur operator consisting of the scaling function of a biorthogonal wavelet set not a likely practical situation in the real world. It cannot be used for compression or expansion which are the main applications of the invention.
Another problem with the methods of this paper is that its computational complexity is high. In order to apply this method for image restoration (or enhancement) large matrices must be calculated (640×480 for an image of this number of pixels) and repeatedly multiplied by all the rows and columns of the image to obtain an enhanced version of it. But, because such a matrix is calculated from a number of ill-conditioned matrices and regularizing techniques must be applied, it is only an initial estimate. To obtain the best possible enhanced image, an iterative procedure, such as the conjugate gradient algorithm, is applied. For these reasons, the method proposed in this paper is impractical even for the expressed purpose of image restoration.
More recently, U.S. application Ser. No. 10/374,824 discloses methods for real-time transmission of high quality digital video and audio signals over networks. This application discloses methods for capturing an image, decimating the image, using, for example, wavelet transformation, time-stamping the image, transmitting a decimated image, decompressing, interpolating and displaying the signal. Decimating the image reduces the number of bits of information that need to be transmitted, thereby increasing speed of transmitting video data over a network. Decompression and interpolation of transmitted information permits reconstruction of images that of high quality, which in some cases, may not be distinguishable by eye from the original, unprocessed image. Coordination of audio signals with decompressed interpolated images permits real-time transmission of video and audio signals to create a high-fidelity display.
However, currently available methods do not provide sufficient protection from unauthorized interception and use by camcorders.